






import numpy as np
from PIL import Image





def  _lowerCamelCase						(						__lowerCamelCase ,							__lowerCamelCase ,							__lowerCamelCase       ) ->			np.ndarray:

     '''simple docstring'''
     UpperCAmelCase__      :  Dict				      =			np.array(__lowerCamelCase       )
     if arr.shape[0] != arr.shape[1]:
          raise ValueError("""The input array is not a square matrix"""       )
     UpperCAmelCase__      :  Optional[int]				      =			0
     UpperCAmelCase__      :  Union[str, Any]				      =			0
     UpperCAmelCase__      :  Optional[Any]				      =			0
     UpperCAmelCase__      :  Optional[int]				      =			0

     # compute the shape of the output matrix
     UpperCAmelCase__      :  Tuple				      =			(arr.shape[0] - size) // stride + 1
     # initialize the output matrix with zeros of shape maxpool_shape
     UpperCAmelCase__      :  Any				      =			np.zeros((maxpool_shape, maxpool_shape)       )

     while i < arr.shape[0]:
          if i + size > arr.shape[0]:
               # if the end of the matrix is reached, break
               break
          while j < arr.shape[1]:
               # if the end of the matrix is reached, break
               if j + size > arr.shape[1]:
                    break
               # compute the maximum of the pooling matrix
               UpperCAmelCase__      :  int				      =			np.max(arr[i : i + size, j : j + size]       )
               # shift the pooling matrix by stride of column pixels
               j += stride
               mat_j += 1

          # shift the pooling matrix by stride of row pixels
          i += stride
          mat_i += 1

          # reset the column index to 0
          UpperCAmelCase__      :  str				      =			0
          UpperCAmelCase__      :  Tuple				      =			0

     return updated_arr





def  _lowerCamelCase						(						__lowerCamelCase ,							__lowerCamelCase ,							__lowerCamelCase       ) ->			np.ndarray:

     '''simple docstring'''
     UpperCAmelCase__      :  Union[str, Any]				      =			np.array(__lowerCamelCase       )
     if arr.shape[0] != arr.shape[1]:
          raise ValueError("""The input array is not a square matrix"""       )
     UpperCAmelCase__      :  Union[str, Any]				      =			0
     UpperCAmelCase__      :  Optional[Any]				      =			0
     UpperCAmelCase__      :  List[str]				      =			0
     UpperCAmelCase__      :  List[Any]				      =			0

     # compute the shape of the output matrix
     UpperCAmelCase__      :  List[Any]				      =			(arr.shape[0] - size) // stride + 1
     # initialize the output matrix with zeros of shape avgpool_shape
     UpperCAmelCase__      :  Dict				      =			np.zeros((avgpool_shape, avgpool_shape)       )

     while i < arr.shape[0]:
          # if the end of the matrix is reached, break
          if i + size > arr.shape[0]:
               break
          while j < arr.shape[1]:
               # if the end of the matrix is reached, break
               if j + size > arr.shape[1]:
                    break
               # compute the average of the pooling matrix
               UpperCAmelCase__      :  int				      =			int(np.average(arr[i : i + size, j : j + size]       )       )
               # shift the pooling matrix by stride of column pixels
               j += stride
               mat_j += 1

          # shift the pooling matrix by stride of row pixels
          i += stride
          mat_i += 1
          # reset the column index to 0
          UpperCAmelCase__      :  List[str]				      =			0
          UpperCAmelCase__      :  Tuple				      =			0

     return updated_arr


# Main Function
if __name__ == "__main__":
      from doctest import testmod

      testmod(name="""avgpooling""", verbose=True)

      # Loading the image
      SCREAMING_SNAKE_CASE__		:							Tuple 				=				Image.open("""path_to_image""")

      # Converting the image to numpy array and maxpooling, displaying the result
      # Ensure that the image is a square matrix

      Image.fromarray(maxpooling(np.array(image), size=3, stride=2)).show()

      # Converting the image to numpy array and averagepooling, displaying the result
      # Ensure that the image is a square matrix

      Image.fromarray(avgpooling(np.array(image), size=3, stride=2)).show()







"""simple docstring"""


import os
from distutils.util import strtobool





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase							)				->   Union[str, Any]:




						'''simple docstring'''



						for e in env_keys:
												lowerCamelCase__   =int(os.environ.get(__lowerCAmelCase  ,       -1							)							)
												if val >= 0:
																		return val
						return default





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase=False							)				->   List[str]:




						'''simple docstring'''



						lowerCamelCase__   =os.environ.get(__lowerCAmelCase  ,       str(__lowerCAmelCase							)							)
						return strtobool(__lowerCAmelCase							) == 1  # As its name indicates `strtobool` actually returns an int...





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase="no"							)				->   Union[str, Any]:




						'''simple docstring'''



						lowerCamelCase__   =os.environ.get(__lowerCAmelCase  ,       str(__lowerCAmelCase							)							)
						return value


